[HTML][HTML] Wireless LAN performance enhancement using double deep Q-networks
K Asaf, B Khan, GY Kim - Applied Sciences, 2022 - mdpi.com
Due to the exponential growth in the use of Wi-Fi networks, it is necessary to study its usage
pattern in dense environments for which the legacy IEEE 802.11 MAC (Medium Access …
pattern in dense environments for which the legacy IEEE 802.11 MAC (Medium Access …
Applying deep reinforcement learning to improve throughput and reduce collision rate in IEEE 802.11 networks
Abstract The effectiveness of Wi-Fi networks is greatly influenced by the optimization of
contention window (CW) parameters. Unfortunately, the conventional approach employed …
contention window (CW) parameters. Unfortunately, the conventional approach employed …
Deep reinforcement learning paradigm for performance optimization of channel observation–based MAC protocols in dense WLANs
The potential applications of deep learning to the media access control (MAC) layer of
wireless local area networks (WLANs) have already been progressively acknowledged due …
wireless local area networks (WLANs) have already been progressively acknowledged due …
[PDF][PDF] Intelligent CW Selection Mechanism Based on Q-Learning (MISQ).
N Zerguine, M Mostefai, Z Aliouat… - Ingénierie des Systèmes …, 2020 - researchgate.net
Accepted: 3 December 2020 Mobile ad hoc networks (MANETs) consist of self-configured
mobile wireless nodes capable of communicating with each other without any fixed …
mobile wireless nodes capable of communicating with each other without any fixed …
Deep reinforcement learning paradigm for dense wireless networks in smart cities
Wireless local area networks (WLANs) are widely deployed for Internet-centric data
applications. Due to their extensive norm in our day-to-day wireless-enabled life, WLANs are …
applications. Due to their extensive norm in our day-to-day wireless-enabled life, WLANs are …
Contention window optimization in IEEE 802.11 ax networks with deep reinforcement learning
W Wydmański, S Szott - 2021 IEEE wireless communications …, 2021 - ieeexplore.ieee.org
The proper setting of contention window (CW) values has a significant impact on the
efficiency of Wi-Fi networks. Unfortunately, the standard method used by 802.11 networks is …
efficiency of Wi-Fi networks. Unfortunately, the standard method used by 802.11 networks is …
Reinforcement learning-based Wi-Fi contention window optimization
SJ Sheila de Cássia, MA Ouameur… - Journal of …, 2023 - jcis.emnuvens.com.br
The collision avoidance mechanism adopted by the IEEE 802.11 standard is not optimal.
The mechanism employs a binary exponential backoff (BEB) algorithm in the medium …
The mechanism employs a binary exponential backoff (BEB) algorithm in the medium …
Reinforcement Learning-based Wi-Fi Contention Window Optimization
SCS Cruz, MA Ouameur, FAP de Figueiredo - 2022 - preprints.org
The collision avoidance mechanism adopted by the IEEE 802.11 standard is not optimal.
The mechanism employs a binary exponential backoff (BEB) algorithm in the medium …
The mechanism employs a binary exponential backoff (BEB) algorithm in the medium …
Improved Video QoE in Wireless Networks Using Deep Reinforcement Learning
Millions of videos are watched per minute on the Internet. Due to real-time performance
demands, such as high-quality video streaming, network administrators face new challenges …
demands, such as high-quality video streaming, network administrators face new challenges …
QoS-oriented media access control using reinforcement learning for next-generation WLANs
J Lei, L Li, Y Wang - Computer Networks, 2022 - Elsevier
Orthogonal frequency division multiple access (OFDMA) is introduced in IEEE 802.11 ax to
satisfy massive transmission demands. However, the uplink OFDMA-based random access …
satisfy massive transmission demands. However, the uplink OFDMA-based random access …